Oncomine_heatmap_GenesCoexpression_one_page_filenames_not_empty<- function(filenames){
  library("textreadr")
  library("stringr")

  #read the long string
  if (grepl(".txt",filenames)) { oncomine=suppressWarnings(paste(readLines(filenames), collapse=" "))
  }else if(grepl(".doc",filenames) | grepl(".docx",filenames)) {oncomine=read_docx(filenames)}

  #get whole data
  whole=str_extract(string =oncomine,pattern = '<area.*\">')#delet hat and tail
  finaldata=strsplit(x = whole,split = '<area')[[1]][-1]#split to unit, after split ,the first list is space so we delet it
  #judge wether each unit has both lenged value, aim to get data in heatmap in middle of the pic
  judge= grepl('Legend Value' ,finaldata)
  #get heatmap data
  heatmapdata=finaldata[judge==TRUE]#the main data

  ####get true true true  true  data
  #get left content as column title
  leftcontenttrue=str_extract_all(string = heatmapdata,pattern = 'leftcontent="[\\w\\s\\:\\|-]*')#extract left content
  leftcontenttrue1=gsub(pattern = 'leftcontent="',replacement = "",x = leftcontenttrue)#delet leftcontent=
  leftcontenttrue2=gsub(pattern = ":",replacement = "",x = leftcontenttrue1)#delet :
  leftcontenttrue3=strsplit(x = leftcontenttrue2,split = "\\|{2}")#split by ||
  leftcontenttrue4=as.data.frame(leftcontenttrue3[1],col.names ='leftcontenttrue3')#creat header for rightcontent4true
  Gene=str_extract(string = leftcontenttrue1,
                   pattern = "[A-Za-z0-9\\|\\*\\~\\!\\@\\#\\$\\%\\^\\&\\*\\)\\)\\-\\_\\,\\.\\;\\'\\:\\?\\>\\<]*")
  #get right data######################################################################
  rightcontenttrue=str_extract_all(string = heatmapdata,
                                   pattern = 'rightcontent="-?[\\d.E-a-za-z\\sA-Z]*(\\|{2}[\\#\\w\\s\'./();,-~&\\*\\@\\!\\`$+{}\\%\\^\\[\\]\\?_]*)*')
  #in pattern,we give character as soon as possible,
  #if oncominebar can not work and give error, remember to add new character to []
  #as follows:
  #Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE,  :
  #                      arguments imply differing number of rows: 3, 4

  rightcontenttrue1=gsub(pattern = 'rightcontent="',replacement = "",x = rightcontenttrue)
  rightcontenttrue2=gsub(pattern = '"',replacement = "",x = rightcontenttrue1)
  rightcontenttrue3=strsplit(x = rightcontenttrue2,split = "\\|{2}")
  #testtttttttttttt,aline
  for (i in 1:length(rightcontenttrue3)){
    b=length(rightcontenttrue3[[i]])
    if (b>nrow(leftcontenttrue4)){
      t1=rightcontenttrue3[[i]][nrow(leftcontenttrue4):b]
      t2=rightcontenttrue3[[i]][1:(nrow(leftcontenttrue4)-1)]
      rightcontenttrue3[[i]]=""
      rightcontenttrue3[[i]]=c(t2,toString(t1))
    }else if( b < nrow(leftcontenttrue4)){
      rightcontenttrue3[[i]][b:nrow(leftcontenttrue4)]='...'
    }
  }
  finaldatatrue1=as.data.frame(rightcontenttrue3,col.names = 1:length(rightcontenttrue3),row.names = leftcontenttrue4[,1])#use leftcontenttrue4 as row name
  #transform
  finaldatatrue2=as.data.frame(t(finaldatatrue1))
  #return data
  rownames(finaldatatrue2)=1:nrow(finaldatatrue2)
  n=grep(pattern = "expression",x = names(finaldatatrue2))
  colnames(finaldatatrue2)[n]="Gene Expression"
  finaldatatrue2[,"Gene Expression"]=as.numeric(as.character(finaldatatrue2[,"Gene Expression"]))
  finaldatawhole=cbind(Gene,finaldatatrue2) # final data
  #caculate correlation
  #get k goal gen
  for (k1 in 1:nrow(finaldatawhole)){
    if (finaldatawhole[k1,1]!=finaldatawhole[k1+1,1]){
      break #just break to get k goal genes in the first file
    }
  }
  if (k1<nrow(finaldatawhole)){
    k=k1
  }

  if (k1<=nrow(finaldatawhole)){
    for (k2 in (k1+1):nrow(finaldatawhole)){
      if (finaldatawhole[k2,1]!=finaldatawhole[k2+1,1]){
        break #just break to get k goal genes in the first file
      }
    }
    if (k2<nrow(finaldatawhole)){
      k=min(k1,k2-k1)
    }
  }
  if (k2<=nrow(finaldatawhole)){
    for (k3 in (k2+1):nrow(finaldatawhole)){
      if (finaldatawhole[k3,1]!=finaldatawhole[k3+1,1]){
        break #just break to get k goal genes in the first file
      }
    }
    if (k3<nrow(finaldatawhole)){
     k=min(k1,k2-k1,k3-k2)
    }
  }
  if (k3<=nrow(finaldatawhole)){
    for (k4 in (k3+1):nrow(finaldatawhole)){
      if (finaldatawhole[k4,1]!=finaldatawhole[k4+1,1]){
        break #just break to get k goal genes in the first file
      }
    }
    if (k4<nrow(finaldatawhole)){
      k=min(k1,k2-k1,k3-k2,k4-k3)
    }

  }
  if (k4<=nrow(finaldatawhole)){
    for (k5 in (k4+1):nrow(finaldatawhole)){
      if (finaldatawhole[k5,1]!=finaldatawhole[k5+1,1]){
        break #just break to get k goal genes in the first file
      }
    }
    if (k5<nrow(finaldatawhole)){
      k=min(k1,k2-k1,k3-k2,k4-k3,k5-k4)
    }

  }
  if (k5<=nrow(finaldatawhole)){
    for (k6 in (k5+1):nrow(finaldatawhole)){
      if (finaldatawhole[k6,1]!=finaldatawhole[k6+1,1]){
        break #just break to get k goal genes in the first file
      }
    }
    if (k6<nrow(finaldatawhole)){
      k=min(k1,k2-k1,k3-k2,k4-k3,k5-k4,k6-k5)
    }
  }
  if (k6<=nrow(finaldatawhole)){
    for (k7 in (k6+1):nrow(finaldatawhole)){
      if (finaldatawhole[k7,1]!=finaldatawhole[k7+1,1]){
        break #just break to get k goal genes in the first file
      }
    }
    if (k7<nrow(finaldatawhole)){
      k=min(k1,k2-k1,k3-k2,k4-k3,k5-k4,k6-k5,k7-k6)
    }
  }
  #caculate correlation
  corloop=nrow(finaldatawhole) %/%  k
  goalgene_expression=finaldatawhole[1:k,2]
  Correlation1=data.frame()
  for (cp in 1:(corloop)){
    if (length(unique(finaldatawhole[(k*(cp-1)+1):(k*cp),1]))==1){
      Correlation2=cor(goalgene_expression,finaldatawhole[(k*(cp-1)+1):(k*cp),2])
      p_value1=cor.test(goalgene_expression,finaldatawhole[(k*(cp-1)+1):(k*cp),2])$p.value
      Correlation1=rbind(Correlation1,cbind(Correlation2,p_value1))
    }else{
      warning("Row from ",(k*(cp-1)+1)," to ",(k*cp)," does not have the same name. Correlation will not be caculated")
      Correlation2='-'
      p_value1='-'
      Correlation1=rbind(Correlation1,cbind(Correlation2,p_value1))
    }
  }

  Correlation=rep(Correlation1[,1],each=k)
  p_value=rep(Correlation1[,2],each=k)
  #to get goal gene left cor
  goalgene_leftn=nrow(finaldatawhole) %%  k
  if (goalgene_leftn!=0){
    goalgene_tail=tail(finaldatawhole$`Gene Expression`, goalgene_leftn)
    cor_tail=cor(goalgene_expression,goalgene_tail)
    p_value1=cor.test(goalgene_expression,goalgene_tail)$p.value
    Correlation=rbind(Correlation,cbind(cor_tail,p_value1))
  }
  coexpressionlast=cbind(Correlation,p_value,finaldatawhole)
  return(coexpressionlast)
}
